In the cloud-assisted Internet of things (IoT), most of the data are sent to the cloud for storage
and processing. Data privacy and security are extreme concerns since retrieving data from …

One of the major open problems in sensor-based Human Activity Recognition (HAR) is the
scarcity of labeled data. Among the many solutions to address this challenge, semi …

Federated learning (FL) is a promising and privacy-preserving distributed learning method
that is widely deployed on edge devices. However, in practical applications, the data …

Pervasive computing promotes the integration of smart devices in our living spaces to
develop services providing assistance to people. Such smart devices are increasingly …

While promoting the intelligence in industrial production, Industry 4.0 has also caused
privacy leaks concurrently. As a possible solution, the existing personalized federated …

Fully decentralized learning enables the distribution of learning resources and decision-
making capabilities across multiple user devices or nodes, and is rapidly gaining popularity …

In this paper, we present a novel blockchain-enabled approach to opportunistic federated
learning (OppCL) for intelligent transportation systems (ITS). Our approach integrates …

In this work, we propose a reconfigurable blockchain (BC)—“PerBlocks”—for handling data
from heterogeneous activities and achieving scalability as well as throughput in the …

Federated learning (FL) enables collaborative training of a shared model on edge devices
while maintaining data privacy. FL is effective when dealing with independent and …

With the rising emergence of decentralized and opportunistic approaches to machine
learning, end devices are increasingly tasked with training deep learning models on-devices …